Bin Packing
HGGA vs INTAGIUM Concept (BETA)
INTAGIUM Concept (Beta) tackles the classic Bin Packing Problem with deterministic intelligence and surgical efficiency through a secure C++ backend.
INTAGIUM Concept is a research-driven project developed by the INTAGIUM team to explore scalable, high-performance solutions for NP-Hard combinatorial problems. Unlike traditional greedy, approximation, or evolutionary methods, our Concept leverages a custom heuristic to construct near-optimal bin packings, fast, clean, and consistent.
It efficiently handles input sets of thousands of items, with varying bin capacities and item distributions. INTAGIUM maintains 100% valid packing logic with strong adherence to theoretical lower bounds achieving benchmark results comparable to metaheuristics like HGGA, but with simpler and faster deterministic code.
Key Features:
High Accuracy: Reaches up to 99% packing efficiency on standard benchmarks
User-Tunable: Accepts any item set and bin capacity with full parameter control
Transparent Output: All bin contents, weights, and performance metrics are saved in .txt reports
Secure Backend: Entirely built in C++, no live code execution, exposed via internal Flask API
Example Output Files:
binpack2_output.txt: Clean report of bins used, capacity fill per bin, total item weight, best-known solution, and time in milliseconds
Why It Matters:
Bin Packing is one of the most studied NP-Hard problems in combinatorial optimization, with real-world applications in logistics, manufacturing, cloud resource allocation, and more.
INTAGIUM Concept proves that near-optimal results can be achieved without evolutionary black-boxes or complex ML models—just smart deterministic logic and lean C++.
Try It Out:
INTAGIUM Concept is currently in Beta. You're invited to test it on your own datasets, inspect the output, and contribute to our vision for next-gen deterministic problem solving.
All benchmarks were run on a standard Intel® i7-12500H laptop and are reproducible.
Benchmarks & Validation:
The algorithm was tested on 1D Bin Packing instances from the OR-Library, a standard reference for optimization research. Performance results were compared against the Hybrid Genetic Grouping Algorithm (HGGA), achieving up to 99% match in bin count with significantly simpler code.
[https://people.brunel.ac.uk/~mastjjb/jeb/orlib/binpackinfo.html]
[https://www.euro-online.org/websites/esicup/data-sets/]
2D Bin Packing extensions are currently under development and will be included in future updates.
Hard28 :
1 D (falkenauer):
Binpack1
Binpack2
Binpack3
Binpack4
Binpack5
Binpack6
Binpack7
Binpack8